Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Feb 21, 2026
Date Accepted: Jul 2, 2026
Radiomics-based AI for predicting neoadjuvant immunochemotherapy pathological response in NSCLC: A systematic review and meta-analysis
ABSTRACT
Background:
Non-small cell lung cancer (NSCLC) remains the leading cause of cancer-related mortality worldwide. Accurate early prediction of response to neoadjuvant therapy is critical.
Objective:
We aimed to evaluate the diagnostic performance of radiomics-based artificial intelligence (AI) in predicting pathological complete response (pCR) and major pathological response (MPR) following neoadjuvant immunochemotherapy in NSCLC and compare it against traditional radiological criteria.
Methods:
A systematic search of PubMed, Embase, Cochrane Library, and Web of Science was conducted through October 26, 2025. Studies utilizing CT or PET/CT-based AI models to predict pCR/MPR were included. Methodological quality was appraised using the PROBAST+AI tool. Sensitivity, specificity, and area under the curve (AUC) were pooled using a bivariate random-effects model.
Results:
Twenty-three studies involving 2004 patients in validation sets were included, with histopathology as the gold standard. For pCR, AI models achieved a pooled sensitivity of 0.77 (95%CI: 0.70–0.83), specificity of 0.79 (95%CI: 0.73–0.84), and AUC of 0.85, significantly outperforming traditional criteria in sensitivity (0.77 vs. 0.42, P < 0.001). For MPR, AI demonstrated a sensitivity of 0.80 (95%CI: 0.72–0.87), specificity of 0.83 (95%CI: 0.73–0.90), and AUC of 0.88, also superior to traditional models (AUC: 0.88 vs. 0.65, P < 0.001). Subgroup analysis revealed that PET/CT-based models offered higher specificity for MPR than CT-based models (0.95 vs. 0.80, P = 0.005).
Conclusions:
Radiomics-based AI demonstrates high diagnostic accuracy and superior sensitivity compared to traditional radiological criteria, showing significant potential for preoperative response assessment. However, the heterogeneity and retrospective design of current studies limit the evidence. Future large-scale, prospective multi-center trials and multi-modal data integration are required to validate these findings for clinical translation. Clinical Trial: PROSPERO (CRD420251243962)
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